Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models

Makoto Aoshima () and Kazuyoshi Yata ()
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Makoto Aoshima: University of Tsukuba
Kazuyoshi Yata: University of Tsukuba

Annals of the Institute of Statistical Mathematics, 2019, vol. 71, issue 3, No 1, 473-503

Abstract: Abstract We consider classifiers for high-dimensional data under the strongly spiked eigenvalue (SSE) model. We first show that high-dimensional data often have the SSE model. We consider a distance-based classifier using eigenstructures for the SSE model. We apply the noise-reduction methodology to estimation of the eigenvalues and eigenvectors in the SSE model. We create a new distance-based classifier by transforming data from the SSE model to the non-SSE model. We give simulation studies and discuss the performance of the new classifier. Finally, we demonstrate the new classifier by using microarray data sets.

Keywords: Asymptotic normality; Data transformation; Discriminant analysis; Large p small n; Noise-reduction methodology; Spiked model (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10463-018-0655-z

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